Unverified50% confidenceFactExact time
The multi-step sampling of diffusion models is inherently slower than single forward-pass regression, and practical deployment often requires acceleration methods such as distillation, few-step sampling, or parallelization.
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10/30/2026
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UnverifiedDiffusion models are more stable to train and produce better generation diversity, but their inference speed is slow (typically requiring hundreds of sampling steps); acceleration methods such as DDIM and consistency models are alleviating this issue.79% similarVerifiedStandard generation with diffusion models may require 50–100 denoising iterations, while consistency models or progressive distillation techniques can reduce the number of steps to 4–878% similarUnverified扩散模型步数越多理论上结果越精细，但耗时也成比例增长，20步是速度与质量的经典平衡点75% similarUnverifiedSpeed optimization often requires combining multiple means such as model distillation, quantization, step reduction, and cache reuse, and these means may affect image quality and generation diversity.74% similarUnverifiedNumerical experiments show that when handling non-smooth and heavy-tailed target distributions, the non-reversible versions exhibit faster sampling efficiency compared to their reversible counterparts.74% similar
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